Frontiers in Robotics and AI (May 2022)
Anti-Disturbance Sliding Mode Control of a Novel Variable Stiffness Actuator for the Rehabilitation of Neurologically Disabled Patients
Abstract
Lower limb exoskeletons are widely used for rehabilitation training of patients suffering from neurological disorders. To improve the human–robot interaction performance, series elastic actuators (SEAs) with low output impedance have been developed. However, the adaptability and control performance are limited by the constant spring stiffness used in current SEAs. In this study, a novel load-adaptive variable stiffness actuator (LaVSA) is used to design an ankle exoskeleton. To overcome the problems of the LaVSA with a larger mechanical gap and more complex dynamic model, a sliding mode controller based on a disturbance observer is proposed. During the interaction process, due to the passive joints at the load side of the ankle exoskeleton, the dynamic parameters on the load side of the ankle exoskeleton will change continuously. To avoid this problem, the designed controller treats it and the model error as a disturbance and observes it with the disturbance observer (DOB) in real time. The first-order derivative of the disturbance set is treated as a bounded value. Subsequently, the parameter adaptive law is used to find the upper bound of the observation error and make corresponding compensation in the control law. On these bases, a sliding mode controller based on a disturbance observer is designed, and Lyapunov stability analysis is given. Finally, simulation and experimental verification are performed. The wearing experiment shows that the resistance torque suffered by humans under human–robot interaction is lower than 120 Nmm, which confirms that the controller can realize zero-impedance control of the designed ankle exoskeleton.
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